Behavioral Patterns in Hypermedia Systems: A Short Study of E-commerce vs. E-learning Practices

Stefani, Antonia; Vassiliadis, Bill; Xenos, Michalis

doi:10.1007/3-540-33279-0_4

Cited by 3 publications

(5 citation statements)

References 21 publications

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“…Adaptation in AHS is based on three approaches: rules, algorithmic methods and hybrid rule-based/algorithmic methods for adaptation (Stefani et al, 2006). Rule based AHS usually use hard-coded rules to supply the user with the necessary adaptation.…”

Section: Personalisation Adaptation and Recommender Systemsmentioning

confidence: 99%

“…This was a major drawback in the rule based approach, which has been replaced or/and complemented with algorithmic methods. Algorithmic methods use machine learning techniques such as data mining for discovering user behaviour (Stefani et al, 2006).…”

Section: Personalisation Adaptation and Recommender Systemsmentioning

confidence: 99%

“…Moreover, the three common types of adaptation in most adaptive hypermedia systems are: content, layout and navigation adaptations (Stefani et al, 2006). Content adaptation is a candidate to exploit the output of the FolksAnnotation tool.…”

Section: Personalisation Adaptation and Recommender Systemsmentioning

confidence: 99%

“…incorporating pedagogical theories. Discussing the similarities and differences between recommender systems and AHS is beyond the scope of this thesis; however the reader is referred to (Stefani et al, 2006).…”

Section: Personalisation Adaptation and Recommender Systemsmentioning

confidence: 99%

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Automatic document-level semantic metadata annotation using folksonomies and domain ontologies

Al‐Khalifa

Rubart

2008

SIGWEB Newsl.

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The last few years have witnessed a fast growth of the concept of Social Software. Be it video sharing such as YouTube, photo sharing such as Flickr, community building such as MySpace, or social bookmarking such as del.icio.us. These websites contain valuable user-generated metadata called folksonomies. Folksonomies are ad hoc, light-weight knowledge representation artefacts to describe web resources using people's own vocabulary. The cheap metadata contained in such websites presents potential opportunities for us (researchers) to benefit from. This thesis presents a novel tool that uses folksonomies to automatically generate metadata with educational semantics in an attempt to provide semantic annotations to bookmarked web resources, and to help in making the vision of the Semantic Web a reality. The tool comprises two components: the tags normalisation process and the semantic annotation process. The tool uses the del.icio.us social bookmarking service as a source for folksonomy tags.The tool was applied to a case study consisting of a framework for evaluating the usefulness of the generated semantic metadata within the context of a particular eLearning application. This implementation of the tool was evaluated over three dimensions: the quality, the searchability and the representativeness of the generated semantic metadata. The results show that folksonomy tags were acceptable for creating semantic metadata. Moreover, folksonomy tags showed the power of aggregating people's intelligence.The novel contribution of this work is the design of a tool that utilises folksonomy tags to automatically generate metadata with fine gained and extra educational semantics.iii

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Section: Personalisation Adaptation and Recommender Systemsmentioning

confidence: 99%

Section: Personalisation Adaptation and Recommender Systemsmentioning

confidence: 99%

Section: Personalisation Adaptation and Recommender Systemsmentioning

confidence: 99%

Section: Personalisation Adaptation and Recommender Systemsmentioning

confidence: 99%

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Automatic document-level semantic metadata annotation using folksonomies and domain ontologies

Al‐Khalifa

Rubart

2008

SIGWEB Newsl.

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“…We extend our initial remarks made in [1] to discuss some of the major similarities and differences between e-learning and e-commerce systems (and particularly B2C systems) that affect adaptation and system usability.…”

Section: Introductionmentioning

confidence: 99%

Adaptation Strategies: A Comparison between E-Learning and E-Commerce Techniques

Vassiliadis

Stefani

2012

IFIP Advances in Information and Communication Technology

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Abstract. The importance of e-learning and e-commerce applications has significantly increased in the past few years. Seeking better design and implementation principles is a research goal with, potentially, a significant impact. One of the commonalities of both applications is user-centricity. Understanding user behavior is critical especially in user-centered applications such as e-commerce and e-learning. In this work we discuss some of the fundamental similarities and differences in e-commerce and formal e-learning adaptation and discuss lessons that could be learned. We argue that current user pattern mining techniques should take into account behavioral and educational theories for distance learning in order to be efficient.Keywords: Adaptation strategies, comparison study, E-learning, E-commerce. IntroductionThe convergence of telecommunication and information technologies has enabled the realization of advanced tools for e-learning, on-line collaboration and content delivery. Two of the most popular applications of web based systems nowadays are elearning and e-commerce. Despite their differences, both types of applications are facing similar challenges: they rely on a "pull" model of information flow, they are hypermedia based, they use similar techniques for adaptation and they benefit from semantic technologies and Knowledge Management. The underlying business models also share the same basic principle: users access digital resources from a distance without the physical presence of a teacher or a seller. Since the importance of both applications has increased, better ways of designing and implementation are needed. Designers, developers and researchers of e-learning systems may learn some useful lessons from the e-commerce domain and vice-versa. There is a lot of common ground between the two types of applications; in this paper the focus is on understanding user behavior and the mechanisms that analyze, model and take advantage of it.

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Case Study

Leino

2012

Educational Recommender Systems and Technologies

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As recommender systems are making inroads to e-learning, the new ecosystem is placing new challenges on them. This Chapter discusses the author’s experiences of adding recommender features to additional reading materials listing page in an undergraduate-level course. Discussion is based on use-log and student questionnaire data. Students could both add materials to lecture readings and peer-evaluate the pertinence of the materials by rating and commenting them. Students were required to add one material and rate five as part of the course requirements. Overall, students perceived the system as useful and did not resent compulsoriness. In addition, perceived social presence promoted social behavior in many students. However, many students rated materials without viewing them, thus undermining the reliability of aggregated ratings. Consequently, while recommenders can enhance the e-learner experience, they need to be robust against some students trying to get points without earning them.

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Behavioral Patterns in Hypermedia Systems: A Short Study of E-commerce vs. E-learning Practices

Cited by 3 publications

References 21 publications

Automatic document-level semantic metadata annotation using folksonomies and domain ontologies

Automatic document-level semantic metadata annotation using folksonomies and domain ontologies

Adaptation Strategies: A Comparison between E-Learning and E-Commerce Techniques

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